It is clear that the continuous improvement in techniques and the search for ever greater efficiency have led to a continued increase in the quantity and complexity of embedded systems in current means of transport, particularly in aircraft. In parallel with the growth in these systems and components, and their respective operational control systems, the number of fault messages generated has risen accordingly. These numerous fault messages generated by the aircraft need to be interpreted so as to decide on the number and effectiveness of the maintenance actions required.
In fact, the many interdependencies between an aircraft's systems, and more generally of any complex real-time system, mean that an actual fault may generate a chain reaction of fault messages throughout the aircraft. These secondary fault messages, deriving from the primary cause of the incident, should not give rise to the maintenance actions normally associated with them because they do not reflect an actual fault in a component, but simply problems in this component's operating conditions as a result of a fault upstream.
If a faulty component is almost always identified, other non-faulty components may also be signaled and give rise to maintenance actions.
Many airlines have indicated that this issue of false fault messages (leading to no-fault-founds) was the most important of the problems for which a solution needed to be found. Indeed, unnecessary checks for parasitic faults result in long immobilizations of aircraft on the ground for maintenance, reducing their time daily flight time, and therefore the profitability of the airline's operations.
Many diagnostics tools have therefore been designed to overcome the problem of faults, whatever the field of activity.
One method quickly stands out from the others: the model-based approach. The principle consists of building the “model” of the systems to be diagnosed and detecting a fault by observing the differences between theoretical and actual input and output events. On this basis, calculations of interactions are carried out in order to target the element or elements in question as well as possible.
Subsequently, new approaches have emerged to refine and develop these model-based methods: hybrid systems. These hybrid systems have been refined by reasoning skills, such as case-based reasoning, Markov chains, etc. These approaches thus make it possible to diagnose systems whose behavior is not known or to handle intermittent fault detections, for instance.
A large proportion of these methods have in common the fact that it is assumed that the “models” of the systems, i.e. the complete logic of their sequences of faults, are known. They then rely on the observation of abnormal events in order to deduce a diagnosis.
In the present case of aeronautical systems comprising a large number of subsystems and components that are arranged in a limited space, whose correct functioning depends on temperature, vibration environment, electrical, chemical, etc parameters, and where a fault may cause local changes in these parameters, the system models are only partially known or formalized.
The initial abnormal event can only be observed through a series of maintenance messages whose causes and consequences are not always clearly identified. This greatly limits the applicability of the model-based approach to this problem of multiple aircraft system faults.